Microsoft Azure Machine Learning Course

I. Supervised Learning

Regression

1. Learn the difference between Regression and Classification
2. Train a Linear Regression model to predict values
3. Predict states using Logistic Regression

Perceptron Algorithms

1. Learn the definition of a perceptron as a building block for neural networks, and the perceptron algorithm for classification

Decision Trees

1. Train Decision Trees to predict states
2. Use Entropy to build decision trees, recursively

Naive Bayes’

1. Learn Bayes’ rule, and apply it to predict cases of spam messages using the Naive Bayes algorithm
2. Train models using Bayesian Learning
3. Complete an exercise that uses Bayesian Learning for natural language processing

Support Vector Machines

1. Train a Support Vector Machines to separate data, linearly
2. Use Kernel Methods in order to train SVMs on data that is not linearly separable

Ensemble of Learners

1. Build data visualizations for quantitative and categorical data
2. Create pie, bar, line, scatter, histogram, and boxplot charts
3. Build professional presentations

Evaluation Metrics

1. Learn about different metrics to measure model success
2. Calculate accuracy, precision, and recall to measure the performance of your models

Training and Tuning Models

1. Train and test models with Scikit-learn
2. Choose the best model using evaluation techniques like cross-validation and grid search

II. Neural Networks

Introduction to Neural Networks

1. Learn the foundations of deep learning and neural networks
2. Implement gradient descent and backpropagation in Python

Implementing Gradient Descent

1. Implement gradient descent using NumPy matrix multiplication

Training Neural Networks

1. Learn several techniques to effectively train a neural network
2. Prevent overfitting of training data and learn best practices for minimizing the error of a network

Deep Learning with PyTorch

1. Learn how to use PyTorch for building deep learning models

III. Unsupervised Learning

Clustering

1. Learn the basics of clustering data
2. Cluster data with the K-means algorithm

Hierarchical and Density-Based Clustering

1. Cluster data with Single Linkage Clustering
2. Cluster data with DBSCAN, a clustering method that captures the insight that clusters are dense group of points

Gaussian Mixture Models

1. Cluster data with Gaussian Mixture Models
2. Optimize Gaussian Mixture Models with and Expectation Maximization

Dimensionality Reduction

1. Reduce the dimensionality of the data using Principal
2. Component Analysis and Independent Component Analysis